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library(ggplot2)
library(plotly)
package 㤼㸱plotly㤼㸲 was built under R version 4.0.4Registered S3 method overwritten by 'data.table':
method from
print.data.table
Registered S3 methods overwritten by 'htmltools':
method from
print.html tools:rstudio
print.shiny.tag tools:rstudio
print.shiny.tag.list tools:rstudio
Registered S3 method overwritten by 'htmlwidgets':
method from
print.htmlwidget tools:rstudio
Attaching package: 㤼㸱plotly㤼㸲
The following object is masked from 㤼㸱package:ggplot2㤼㸲:
last_plot
The following object is masked from 㤼㸱package:stats㤼㸲:
filter
The following object is masked from 㤼㸱package:graphics㤼㸲:
layout
source("./DataProccess.R")
Registered S3 methods overwritten by 'dbplyr':
method from
print.tbl_lazy
print.tbl_sql
-- Attaching packages -------------------------------------------------------------------------------------------------------------- tidyverse 1.3.0 --
v tibble 3.0.5 v dplyr 1.0.3
v tidyr 1.1.2 v stringr 1.4.0
v readr 1.4.0 v forcats 0.5.0
v purrr 0.3.4
-- Conflicts ----------------------------------------------------------------------------------------------------------------- tidyverse_conflicts() --
x dplyr::filter() masks plotly::filter(), stats::filter()
x dplyr::lag() masks stats::lag()
Attaching package: 㤼㸱zoo㤼㸲
The following objects are masked from 㤼㸱package:base㤼㸲:
as.Date, as.Date.numeric
Loading required package: ggstance
Attaching package: 㤼㸱ggstance㤼㸲
The following objects are masked from 㤼㸱package:ggplot2㤼㸲:
geom_errorbarh, GeomErrorbarh
Loading required package: scales
Attaching package: 㤼㸱scales㤼㸲
The following object is masked from 㤼㸱package:purrr㤼㸲:
discard
The following object is masked from 㤼㸱package:readr㤼㸲:
col_factor
Loading required package: ggridges
New to ggformula? Try the tutorials:
learnr::run_tutorial("introduction", package = "ggformula")
learnr::run_tutorial("refining", package = "ggformula")
Attaching package: 㤼㸱lubridate㤼㸲
The following objects are masked from 㤼㸱package:base㤼㸲:
date, intersect, setdiff, union
HFGData="../data/HFG data for stats preliminary 3-18-21.xlsx"
HFGFrame=HFGInfo(HFGData)%>%
mutate("Filter Rep"=as.character(`Filter Rep`),`Well Rep`=as.character(`Well Rep`))
New names:
* `` -> ...2
HFGFrame.mean=HFGFrame%>%
ungroup()%>%
group_by(Date,`Filter Rep`,Plant)%>%
summarise(N1GC=mean(N1GC))%>%
ungroup()%>%
filter(N1GC<10000000)
`summarise()` has grouped output by 'Date', 'Filter Rep'. You can override using the `.groups` argument.
HFGFrame.Filtered=HFGFrame%>%
filter(N1GC<100000000)%>%
filter(!is.na(N1GC),!is.na(Date))
HFGDFrame=HFGFrame.Filtered
p=ggplot()+
geom_jitter(data=HFGDFrame,aes(x=Date, y=N1GC , color=`Filter Rep`,text=Plant),size=1, position = position_jitter(width = .1, height = 0))+
geom_smooth(data=HFGFrame.mean,aes(x=Date,y=N1GC, color=`Filter Rep`),se=F)
Ignoring unknown aesthetics: text
ggplotly(p,tooltip = c("text", "size"))
`geom_smooth()` using method = 'loess' and formula 'y ~ x'